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Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development. [ABSTRACT FROM AUTHOR]

Introduction: Metabolic inducers can expose people with polypharmacy to adverse health outcomes. A limited fraction of potential drug–drug interactions (DDIs) have been or can ethically be studied in clinical trials, leaving the vast majority unexplored. In the present study, an algorithm has been developed to predict the induction DDI magnitude, integrating data related to drug-metabolising enzymes. Methods: The area under the curve ratio (AUCratio) resulting from the DDI with a victim drug in the presence and absence of an inducer (rifampicin, rifabutin, efavirenz, or carbamazepine) was predicted from various in vitro parameters and then correlated with the clinical AUCratio (N = 319). In vitro data including fraction unbound in plasma, substrate specificity and induction potential for cytochrome P450s, phase II enzymes and uptake, and efflux transporters were integrated. To represent the interaction potential, the in vitro metabolic metric (IVMM) was generated by combining the fraction of substrate metabolised by each hepatic enzyme of interest with the corresponding in vitro fold increase in enzyme activity (E) value for the inducer. Results: Two independent variables were deemed significant and included in the algorithm: IVMM and fraction unbound in plasma. The observed and predicted magnitudes of the DDIs were categorised accordingly: no induction, mild, moderate, and strong induction. DDIs were assumed to be well classified if the predictions were in the same category as the observations, or if the ratio between these two was < 1.5-fold. This algorithm correctly classified 70.5% of the DDIs. Conclusion: This research presents a rapid screening tool to identify the magnitude of potential DDIs utilising in vitro data which can be highly advantageous in early drug development. [ABSTRACT FROM AUTHOR]

Background and Objectives: Technologies for long-acting administration of antiretrovirals (ARVs) for the prevention and treatment of HIV are at the forefront of research initiatives aiming to tackle issues surrounding drug adherence with the current standard of once-daily oral administration. Islatravir (ISL) is an emerging ARV that shows promising characteristics for long-acting prevention and treatment both orally as well as through alternative routes of administration. Microneedle array patches (MAPs) are a pain-free and discreet transdermal delivery technology that offer extended-release administration of nanoparticulate drugs. This study aimed to utilise physiologically based pharmacokinetic (PBPK) modelling to predict the pharmacokinetics resulting from ISL administered via MAP and to identify key MAP characteristics required to sustain effective concentrations over extended dosing intervals. Methods: A PBPK model describing the conversion of ISL to ISL-triphosphate (ISL-TP) and its whole-body disposition was developed and verified against observed clinical data for orally administered ISL in healthy adults. An intradermal PBPK model was integrated with the ISL PBPK model to predict the dose and nanoparticle release rate required for MAP administration strategies capable of achieving a minimum ISL-TP target concentration of 0.05 pmol/106 PBMCs over extended dosing intervals. MAP design was limited to a maximum therapeutic area of 20 cm2 with a dose loading of 4.09 mg/cm2 and a minimum duration of 3 months. Due to the lack of available clinical data, a range of nanoparticle release rates and MAP bioavailability scenarios were simulated to provide an overview of potential clinical outcomes. Results: The ISL PBPK model was successfully verified, with predicted vs observed ratios falling within 0.5–2-fold. ISL MAP doses ranging from 15 to 80 mg were predicted to sustain ISL-TP concentrations above the minimum target concentration at 3, 6 and 12 months after administration. Nanoparticle release rate and MAP bioavailability were found to have a major impact on whether dosing strategies achieved the criteria. Minimum doses of 15 mg and 60 mg with a nanoparticle release rate of 0.0005 h−1 and bioavailability ranging from 25 to 100% were predicted to achieve effective ISL-TP concentrations up to 3 and 6 months, respectively. Doses of 15 mg and 30 mg with a nanoparticle release rate of 0.0005 h−1 were also able to attain the target concentration up to 6 months after MAP administration, albeit with a minimum bioavailability of 75% and 50%, respectively. Furthermore, when simulating a bioavailability of 100%, an 80 mg ISL MAP was predicted to sustain ISL-TP concentrations above the minimum target concentration up to 12 months after administration. Conclusions: The ISL PBPK model successfully predicted ISL and ISL-TP pharmacokinetics across a range of orally administered regimens. The integrated intradermal PBPK model outlined optimal MAP dose and nanoparticle release rates for effective ISL-TP concentrations up to 12 months, providing justification for further investigation of ISL as a candidate for MAP administration. [ABSTRACT FROM AUTHOR]

The use of nanoparticles (NPs) can support an enhancement of drug distribution, resulting in increased drug penetration into key tissues. Experimental in vitro data can be integrated into computational approaches to simulate NP absorption, distribution, metabolism and elimination (ADME) processes and provide quantitative pharmacokinetic predictions. The aim of this study is to develop a novel mechanistic and physiologically based pharmacokinetic (m-PBPK) model to predict the biodistribution of NPs focusing on Doxil. The main processes underpinning NPs ADME were represented considering molecular and cellular mechanisms such as stability in biological fluids, passive permeability and uptake activity by macrophages. A whole-body m-PBPK rat and human models were designed in Simbiology v. 9.6.0 (MATLAB R2019a). The m-PBPK models were successfully qualified across doxorubicin and Doxil® in both rat and human since all PK parameters AUC0-inf, Cmax, t1/2, Vd and Cl were within twofold, with an AUC0-inf absolute average-fold error (AAFE) value of 1.23 and 1.16 and 1.76 and 1.05 for Doxorubicin and Doxil® in rat and human, respectively. The time to maximum concentration in tissues for doxorubicin in both rat and human models was before 30 min of administration, while for Doxil®, the tmax was after 24 h of administration. The organs that accumulate most NP are the spleen, liver and lungs, in both models. The m-PBPK represents a predictive platform for the integration of in vitro and formulation parameters in a physiological context to quantitatively predict the NP biodistribution. Schematic diagram of the whole-body m-PBPK models developed for Doxil® in rat and human physiology. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to simulate the drug–drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis. Methods: A whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (Ctrough) clinical cut-off of 150 ng/mL. Results: The PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV Ctrough, with > 95% of individuals predicted to achieve Ctrough above the clinical cut-off. Conclusions: The developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the Ctrough of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients. [ABSTRACT FROM AUTHOR]

Introduction: In December 2019, an outbreak of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began, resulting in a number of antivirals and immune modulators being repurposed to treat the associated coronavirus disease 2019 (COVID-19). Many patients requiring treatment for COVID-19 may have either pre-existing renal or hepatic disease or experience acute renal/hepatic injury as a result of the acute infection. Altered renal or hepatic function can significantly affect drug concentrations of medications due to impaired drug metabolism and excretion, resulting in toxicity or reduced efficacy. The aim of this paper is to review the pharmacokinetics and available study data for the experimental COVID-19 therapies in patients with any degree of renal or hepatic impairment to make recommendations for dosing. Methods: COVID-19 agents included in these recommendations were listed as primaries on the University of Liverpool COVID-19 drug interaction website (www.covid19-druginteractions.org), initially identified from Clinicialtrials.gov and ChicCTR.org.cn. A literature search was performed using PubMed and EMBASE as well as product licences and pharmacokinetic databases. Findings: Remdesivir, dexamethasone, azithromycin, favipiravir, lopinavir/ritonavir, atazanavir, hydroxychloroquine, interferon beta, ribavirin, tocilizumab, anakinra and sarilumab were identified as experimental drugs being used in COVID-19 trials as of November 2020. Limited study data was found for these drugs in patients with renal or hepatic impairment for COVID-19 or other indications. Recommendations were made based on available data, consideration of pharmacokinetic properties (including variability), the dosing and anticipated treatment duration of each regimen in COVID-19 and known toxicities. Conclusion: Dosing of drugs used to treat COVID-19 in patients with renal or hepatic impairment is complex. These recommendations were produced to provide guidance to clinicians worldwide who are treating patients with COVID-19, many of whom will have some degree of acute or chronic renal or hepatic impairment. [ABSTRACT FROM AUTHOR]

Background: Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes.Objective: The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework.Methods: PBPK models for all ten drugs were developed in young adults (20-50 years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65 years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [Cmax], time to Cmax [tmax], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs.Results: The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20 years, which was explained by decreased clearance, while Cmax, tmax and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter.Conclusion: The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug. [ABSTRACT FROM AUTHOR]

Background: Aging is characterized by anatomical, physiological, and biological changes that can impact drug kinetics. The elderly are often excluded from clinical trials and knowledge about drug kinetics and drug-drug interaction magnitudes is sparse. Physiologically based pharmacokinetic modeling can overcome this clinical limitation but detailed descriptions of the population characteristics are essential to adequately inform models.Objective: The objective of this study was to develop and verify a population database for aging Caucasians considering anatomical, physiological, and biological system parameters required to inform a physiologically based pharmacokinetic model that included population variability.Methods: A structured literature search was performed to analyze age-dependent changes of system parameters. All collated data were carefully analyzed, and descriptive mathematical equations were derived.Results: A total of 362 studies were found of which 318 studies were included in the analysis as they reported rich data for anthropometric parameters and specific organs (e.g., liver). Continuous functions could be derived for most system parameters describing a Caucasian population from 20 to 99 years of age with variability. Areas with sparse data were identified such as tissue composition, but knowledge gaps were filled with plausible qualified assumptions. The developed population was implemented in Matlab® and estimated system parameters from 1000 virtual individuals were in accordance with independent observed data showing the robustness of the developed population.Conclusions: The developed repository for aging subjects provides a singular specific source for key system parameters needed for physiologically based pharmacokinetic modeling and can in turn be used to investigate drug kinetics and drug-drug interaction magnitudes in the elderly. [ABSTRACT FROM AUTHOR]

Background and Objectives: Long-acting injectable antiretrovirals represent a pharmacological alternative to oral formulations and an innovative clinical option to address adherence and reduce drug costs. Clinical studies in children and adolescents are characterised by ethical and logistic barriers complicating the identification of dose optimisation. Physiologically-based pharmacokinetic modelling represents a valuable tool to inform dose finding prior to clinical trials. The objective of this study was to simulate potential dosing strategies for existing long-acting injectable depot formulations of cabotegravir and rilpivirine in children and adolescents (aged 3-18 years) using physiologically-based pharmacokinetic modelling.Methods: Whole-body physiologically-based pharmacokinetic models were developed to represent the anatomical, physiological and molecular processes and age-related changes in children and adolescents through allometric equations. Models were validated for long-acting injectable intramuscular cabotegravir and rilpivirine in adults. Subsequently, the anatomy and physiology of children and adolescents were validated against available literature. The optimal doses of monthly administration of cabotegravir and rilpivirine were identified in children and adolescents, to achieve trough concentrations over the target concentrations derived in a recent efficacy trial of the same formulations.Results: Pharmacokinetic data generated through the physiologically-based pharmacokinetic simulations were similar to observed clinical data in adults. Optimal doses of long-acting injectable antiretrovirals cabotegravir and rilpivirine were predicted using the release rate observed for existing clinical formulations, for different weight groups of children and adolescents. The intramuscular loading dose and maintenance dose of cabotegravir ranged from 200 to 600 mg and from 100 to 250 mg, respectively, and for rilpivirine it ranged from 250 to 550 mg and from 150 to 500 mg, respectively, across various weight groups of children ranging from 15 to 70 kg.Conclusions: The reported findings represent a rational platform for the identification of suitable dosing strategies and can inform prospective clinical investigation of long-acting injectable formulations in children and adolescents. [ABSTRACT FROM AUTHOR]

Background: Antiretroviral drugs are among the therapeutic agents with the highest potential for drug-drug interactions (DDIs). In the absence of clinical data, DDIs are mainly predicted based on preclinical data and knowledge of the disposition of individual drugs. Predictions can be challenging, especially when antiretroviral drugs induce and inhibit multiple cytochrome P450 (CYP) isoenzymes simultaneously.Methods: This study predicted the magnitude of the DDI between efavirenz, an inducer of CYP3A4 and inhibitor of CYP2C8, and dual CYP3A4/CYP2C8 substrates (repaglinide, montelukast, pioglitazone, paclitaxel) using a physiologically based pharmacokinetic (PBPK) modeling approach integrating concurrent effects on CYPs. In vitro data describing the physicochemical properties, absorption, distribution, metabolism, and elimination of efavirenz and CYP3A4/CYP2C8 substrates as well as the CYP-inducing and -inhibitory potential of efavirenz were obtained from published literature. The data were integrated in a PBPK model developed using mathematical descriptions of molecular, physiological, and anatomical processes defining pharmacokinetics. Plasma drug-concentration profiles were simulated at steady state in virtual individuals for each drug given alone or in combination with efavirenz. The simulated pharmacokinetic parameters of drugs given alone were compared against existing clinical data. The effect of efavirenz on CYP was compared with published DDI data.Results: The predictions indicate that the overall effect of efavirenz on dual CYP3A4/CYP2C8 substrates is induction of metabolism. The magnitude of induction tends to be less pronounced for dual CYP3A4/CYP2C8 substrates with predominant CYP2C8 metabolism.Conclusion: PBPK modeling constitutes a useful mechanistic approach for the quantitative prediction of DDI involving simultaneous inducing or inhibitory effects on multiple CYPs as often encountered with antiretroviral drugs. [ABSTRACT FROM AUTHOR]

The images or other third-party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. Correction to: Predicting Drug-Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. [Extracted from the article]

Background and Objectives: Antiretrovirals are currently used for the treatment and prevention of HIV infection. However, poor adherence and low tolerability of some existing oral formulations can hinder their efficacy. Long-acting (LA) injectable nanoformulations could help address these complications by simplifying antiretroviral administration. The aim of this study is to inform the optimisation of intramuscular LA formulations for eight antiretrovirals through physiologically based pharmacokinetic (PBPK) modelling. Methods: A whole-body PBPK model was constructed using mathematical descriptions of molecular, physiological and anatomical processes defining pharmacokinetics. These models were validated against available clinical data and subsequently used to predict the pharmacokinetics of injectable LA formulations Results: The predictions suggest that monthly intramuscular injections are possible for dolutegravir, efavirenz, emtricitabine, raltegravir, rilpivirine and tenofovir provided that technological challenges to control their release rate can be addressed. Conclusions: These data may help inform the target product profiles for LA antiretroviral reformulation strategies. [ABSTRACT FROM AUTHOR]

Background and Objectives: In clinical practice, antiretroviral regimens are often interrupted or modified for intolerance and toxicity. The objective of this study was to develop an in vitro to in vivo extrapolation (IVIVE) approach to describe the interaction when efavirenz is switched to either maraviroc or nevirapine and to test different switching scenarios to identify the best strategy. Methods: In vitro data describing the chemical and absorption, tissue distribution, metabolism and excretion (ADME) characteristics of efavirenz, maraviroc and nevirapine were obtained from the literature, and used to simulate plasma exposures of these drugs using the Simcyp Population-Based Simulator. The predicted maraviroc and nevirapine exposures were compared with data from clinical studies evaluating their exposures following a switch from efavirenz. Results: Model predictions for maraviroc and nevirapine exposure were in agreement with observed data. The simulations suggest that the waning efavirenz induction effect following discontinuation necessitated increasing maraviroc to 600 mg twice daily for 1 week after efavirenz cessation. Alternatively, adequate exposure of maraviroc was shown with a dose of 450 mg for 2 weeks. Efavirenz waning induction did not affect nevirapine exposure. Conclusion: IVIVE modelling successfully predicted patient drug exposure. This modelling technique is able to inform the design of clinical studies, and allows assessment of pragmatic dosing strategies under complex therapeutic scenarios. [ABSTRACT FROM AUTHOR]

Background and Objective: The rate of depression in patients with HIV is higher than in the general population. The use of antidepressants can have a beneficial effect, improving antiretroviral therapy adherence and consequently their efficacy and safety. Efavirenz and protease inhibitor boosted with ritonavir are major components of the antiretroviral therapy and are inducers and/or inhibitors of several cytochrome P450 (CYP) isoforms. Although antidepressants are prescribed to a significant proportion of patients treated with antiretrovirals, there are limited clinical data on drug-drug interactions. The aim of this study was to predict the magnitude of drug-drug interactions among efavirenz, boosted protease inhibitors and the most commonly prescribed antidepressants using an in vitro-in vivo extrapolation (IVIVE) model simulating virtual clinical trials. Methods: In vitro data describing the chemical characteristics, and absorption, distribution, metabolism and elimination (ADME) properties of efavirenz, boosted protease inhibitors and the most commonly prescribed antidepressants were obtained from published literature or generated by standard methods. Pharmacokinetics and drug-drug interaction were simulated using the full physiologically based pharmacokinetic model implemented in the Simcyp™ ADME simulator. The robustness of our modeling approach was assessed by comparing the magnitude of simulated drug-drug interactions using probe drugs to that observed in clinical studies. Results: Simulated pharmacokinetics and drug-drug interactions were in concordance with available clinical data. Although the simulated drug-drug interactions with antidepressants were overall weak to moderate according to the classification of the US FDA, fluoxetine and venlafaxine represent better candidates from a pharmacokinetic standpoint for patients on efavirenz and venlafaxine or citalopram for patients on boosted protease inhibitors. Conclusion: The modest magnitude of interaction could be explained by the fact that antidepressants are substrates of multiple isoforms and thus metabolism can still occur through CYPs that are weakly impacted by efavirenz or boosted protease inhibitors. These findings indicate that IVIVE is a useful tool for predicting drug-drug interactions and designing prospective clinical trials, giving insight into the variability of exposure, sample size and time-dependent induction or inhibition. [ABSTRACT FROM AUTHOR]

A simultaneous extraction method to measure daptomycin, amikacin, gentamicin, and rifampicin in human plasma, by high-performance liquid chromatography, was developed and validated. The method involved a rapid sample preparation by protein precipitation with acetonitrile followed by direct injection into a high-performance liquid chromatography system coupled with mass detection. Drug retention times were 10.00 ± 0.25, 2.00 ± 0.25, 3.50 ± 0.25, 11.50 ± 0.25, and 12.50 ± 0.25 min for daptomycin, amikacin, gentamicin, rifampicin, and quinoxaline, respectively. Good linearity (mean r2 = 0.998) was obtained for all drugs quantified over the range of clinically relevant concentrations in human plasma and the use of the internal standard quinoxaline improves accuracy (RSD% <14.9%) and intra-day (RSD% <11.56) and inter-day (RSD% <12.10) precision for the analytical procedure. The limits of quantification for daptomycin, amikacin, gentamicin, and rifampicin were 1.56, 2.34, 0.63, 0.63 μg/ml, respectively. Moreover, the addition of ion pair trifluoroacetic acid in the sample allowed the majority of gentamicin and amikacin separation. A rapid, specific, sensitive, accurate, and reproducible HPLC method was developed and validated to measure daptomycin, amikacin, gentamicin, and rifampicin in human plasma. This method is suitable for clinical pharmacokinetic studies. [ABSTRACT FROM AUTHOR]

Background and objective: In the treatment of bone infections, a major determinant of the clinical response is the active drug concentration at the infected site. Because of the high prevalence of meticillin (methicillin)- resistant staphylococci and enterococci, glycopeptides are widely used for the treatment of bone and joint infections, but data on their penetration into human bone are lacking. The aim of our study was to measure vancomycin and teicoplanin concentrations in infected human bone under steady-state conditions and verify their relationship with inflammatory markers, patient demographic characteristics and pharmacodynamic microbiological markers. Methods and patients: Twenty-seven adult orthopaedic patients undergoing surgical debridement for septic pseudoarthrosis of the tibia and receiving either intravenous vancomycin (Vancocina® 1 g twice daily) or teicoplanin (Targosid® 10 mg/kg/day) were studied from January 2004 to January 2008. Plasma and bone specimens were simultaneously collected during surgery for pharmacokinetic and microbiological assays at a variable interval after antimicrobial administration. Bone samples were dissected into cortical and cancellous bone, cleaned of soft tissues, crushed and eluted into phosphate buffer. Necrotic samples and sequestra were not analysed. Plasma and bone antimicrobial concentrations were measured by a validated method of high-performance liquid chromatography with UV detection, and bone/plasma concentration ratios were calculated. Cortical and cancellous bone area under the concentration-time curve (AUC) over 24 hours (AUC24) values were measured by the linear-log trapezoidal rule, using WinNonlin® software, and were compared with the minimum inhibitory concentrations (MICs) of the infecting agents. Results: For vancomycin, the mean ± SD concentrations were 2.66 ± 1.2 mg/L in cortical bone and 11.53 ± 7.8 mg/L in cancellous bone (corresponding to 20.67% and 89.39% of intraoperative plasma concentrations), and the mean ± SD tissue AUC24 values were 55.15 ± 25.26 h • mg/L for cortical bone and 299.16 ± 299.54 h • mg/L for cancellous bone. For teicoplanin, the mean ± SD concentrations were 2.01 ± 1.7 and 7.51 ± 7.0 mg/L in cortical and cancellous bone, respectively (12.35% and 48.6% of intraoperative plasma concentrations), and the mean ± SD teicoplanin tissue AUC24 values were 34.08 ± 23.6 h • mg/L and 155.17 ± 132.8 h • mg/L for cortical bone and cancellous bone, respectively. The mean vancomycin AUC24/MIC ratios were 215.02 for plasma, 47.14 for cortical bone and 268.95 for cancellous bone. The mean teicoplanin AUC24/MIC ratios were 336.48, 36.27 and 197.21 for plasma, cortical bone and cancellous bone, respectively. Conclusions: Bone penetration of both glycopeptides ranged from poor (<15%) to satisfactory (15-30%) in the cortical compartment, while it was far higher into the highly vascularized cancellous tissue. Vancomycin bone penetration was slightly higher than with teicoplanin, but the difference was not statistically significant. Higher bone concentrations were observed with higher inflammatory markers, possibly as a result of increased vascularization and vascular permeability under inflammatory conditions. Bone concentrations over the MIC and AUC/MIC ratios suggested that both glycopeptides achieve a satisfactory pharmacokinetic exposure in the cancellous bone, as far as Gram-positive pathogens are concerned. On the other hand, cortical bone exposure was suboptimal in most patients. Furthermore, as antimicrobial penetration may be affected by impaired blood supply, the role of radical surgical removal of purulent and necrotic tissues appears to be essential in order to shorten treatment duration and to reduce the risk of treatment failure. [ABSTRACT FROM AUTHOR]

The increasing global prevalence of human immunodeficiency virus (HIV) is estimated at 36.7 million people currently infected. Lifelong antiretroviral (ARV) drug combination dosing allows management as a chronic condition by suppressing circulating viral load to allow for a near-normal life; however, the daily burden of oral administration may lead to non-adherence and drug resistance development. Long-acting (LA) depot injections of nanomilled poorly water-soluble ARVs have shown highly promising clinical results with drug exposure largely maintained over months after a single injection. ARV oral combinations rely on water-soluble backbone drugs which are not compatible with nanomilling. Here, we evaluate a unique prodrug/nanoparticle formation strategy to facilitate semi-solid prodrug nanoparticles (SSPNs) of the highly water-soluble nucleoside reverse transcriptase inhibitor (NRTI) emtricitabine (FTC), and injectable aqueous nanodispersions; in vitro to in vivo extrapolation (IVIVE) modelling predicts sustained prodrug release, with activation in relevant biological environments, representing a first step towards complete injectable LA regimens containing NRTIs. Non-adherence to daily drug regimens is responsible for many negative clinical effects including drug resistance in life-long treatments for HIV. Here, the authors report on a HIV prodrug nanoparticle platform for long-acting sustained release of water-soluble drug compounds following injection. [ABSTRACT FROM AUTHOR]